class_docstr: An implementation of `"NEU" <https://www.ijcai.org/Proceedings/2017/0544.pdf>`_
  from the IJCAI 17 paper "Fast Network Embedding Enhancement via High Order Proximity Approximation".
  The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive
  meta learning algorithm.
  
  Args:
      L1 (float): Weight of lower order proximities. Defauls is 0.5
      L2 (float): Weight of higher order proximities. Default is 0.25.
      T (int): Number of iterations. Default is 1.
      seed (int): Random seed value. Default is 42.
docstr: Normalizes matrix rows by their Frobenius norm.
  Args:
      original_embedding (Numpy array): An array containing an embedding
  
  Return types:
      normalized_embedding (Numpy array): An array containing a normalized embedding
docstr_tok: ["Normalizes","matrix","rows","by","their","Frobenius","norm",".","Args",":","original_embedding","(","Numpy","array",")",":","An","array","containing","an","embedding","Return","types",":","normalized_embedding","(","Numpy","array",")",":","An","array","containing","a","normalized","embedding"]
code: def _normalize_embedding(self, original_embedding):
      r"""Normalizes matrix rows by their Frobenius norm.
      Args:
          original_embedding (Numpy array): An array containing an embedding
  
      Return types:
          normalized_embedding (Numpy array): An array containing a normalized embedding
      """
      norms = np.linalg.norm(original_embedding, axis=1)
      normalized_embedding = (original_embedding.T / norms).T
      return normalized_embedding
  
file_path: karateclub/karateclub/node_embedding/meta/neu.py
imports: import numpy
  import networkx
  from sklearn.preprocessing import normalize
  from karateclub.estimator import Estimator
label_desc: Use image node_id 2 for calling the NEU obj's underlying member method code with example usage: obj._normalize_embedding(original_embedding) and returns: normalized_embedding